Optimization of natural rubber foams: Effect of foaming agent content and processing conditions on the cellular structure and mechanical properties
Why this work is in the frame
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Bibliographic record
Abstract
In the past decades, natural rubber (NR) foams became popular in the automotive, construction and aerospace industries because of their lightweight, flexibility and shock-absorbing properties. The selection of optimal formulation and processing parameters is critical to produce foam with specific properties depending on the application. In this study, the effect of foaming agent concentration, foaming temperature and time on the morphological and mechanical properties of NR foams was investigated. First, increasing the foaming agent content from 5 to 9 phr (parts per hundred rubber) increased the cell size (16%), while decreasing the compression modulus (28%). In the second part, increasing the foaming temperature (145 to 155°C) resulted in larger cell size (163%); while decreasing the cell density (28%), compression modulus (2%), and hardness (1%). In the third part, increasing the foaming time (25 to 45 min) led to smaller cell size (63%) combined with higher cell density (100%), compression modulus (16%), and hardness (3%). Based on all the results obtained, the best NR foam was obtained with 7 phr of foaming agent and produced at 150°C for 35 min leading to superior morphological and mechanical performance: the smallest cell size (25 µm) and the most uniform cell size distribution ( Đ = 1.03) generating the highest compression modulus (3.36 MPa). Finally, the experimental compression results were combined to build a nonlinear regression model to optimize the formulation and processing conditions leading to 6.5 phr of OBSH molded at 150°C for 36 min. The model showed good agreement with a validation test with less than 2% deviation observed for both compression modulus and strength.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it